Uncertainty assessment through a precipitation dependent hydrologic uncertainty processor: An application to a small catchment in southern Italy
Adequate assessment of uncertainty for prediction and simulation purposes is a current issue in hydrological research. This article describes the application of the Hydrologic Uncertainty Processor (HUP) proposed by Krzystofowicz in 1999 to a small semi-arid watershed in southern Italy. The version...
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Veröffentlicht in: | Journal of hydrology (Amsterdam) 2010-05, Vol.386 (1), p.38-54 |
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Sprache: | eng |
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Zusammenfassung: | Adequate assessment of uncertainty for prediction and simulation purposes is a current issue in hydrological research. This article describes the application of the Hydrologic Uncertainty Processor (HUP) proposed by Krzystofowicz in 1999 to a small semi-arid watershed in southern Italy. The version applied in this work is a precipitation-dependent HUP aimed at assessing the hydrologic uncertainty about actual streamflow at some future time, with lead times of a few hours, given the information available at the forecast time and assuming a perfectly known amount of precipitation. The processor is based on Bayes theorem and hence models the prior and likelihood functions to obtain the revised posterior distribution.
A complete example of the modelling assumptions, estimation procedure and results is carried out in the present paper. In detail, we analysed a 26-km
2 semi-arid basin, considering hourly forecasts over an almost continuous five-year period in 2000–2005. A distributed rainfall–runoff model suited to represent contributions of different runoff generation mechanisms to hydrologic response is used for deterministic predictions. Analysis of the resulting posterior distributions show that hydrologic uncertainty: (i) grows with the value of discharge predicted by the model; (ii) is higher when associated with high precipitation amounts; and (iii) increases with lead time of predictions. The predictive ability of the processor is investigated for several runoff events. The results indicate good processor performance for a lead time equal to the period covered by the precipitation forecast, and a significant deterioration for higher lead times that is heavily dominated by the presumption of null precipitation beyond the forecast period. Finally, the skill of the processor is assessed through a retrospective analysis in terms of the probability of detection and the false-alarm rate. |
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ISSN: | 0022-1694 1879-2707 |
DOI: | 10.1016/j.jhydrol.2010.03.004 |